Crisis in science due to reliance on software that identifies patterns that exist only in a certain data set and not the real world. If mistakes are found it’s by chance and much later-BBC, AAAS presentation in Washington

Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”.

She warned scientists that if they didn’t improve their techniques they would be wasting both time and money. Her research was presented at the American Association for the Advancement of Science in Washington.

Machine learning systems and the use of big data sets has accelerated the crisis, according to Dr Allen. That is because machine learning algorithms have been developed specifically to find interesting things in datasets and so when they search through huge amounts of data they will inevitably find a pattern.

“The challenge is can we really trust those findings?” she told BBC News.

Dr Allen is working with a group of biomedical researchers at Baylor College of Medicine in Houston to improve the reliability of their results. She is developing the next generation of machine learning and statistical techniques that can not only sift through large amounts of data to make discoveries, but also report how uncertain their results are and their likely reproducibility.